| Challenge: | Existing models that improve formal and informal language understanding tasks do not transfer to informal data directly. |
| Approach: | They propose a data annealing transfer learning procedure to bridge the performance gap on informal natural language understanding tasks. |
| Outcome: | The proposed procedure outperforms state-of-the-art models on three common tasks. |
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| Challenge: | Recent work on deep NLP models has centered on probing, a method that involves training classifiers for different tasks on model representations. |
| Approach: | They propose a method for determining the impact of each pretrained layer on transfer task performance by ablation. |
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An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)
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| Challenge: | Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures. |
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Transfer Learning in Natural Language Processing (N19-5)
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| Challenge: | supervised machine learning is based on learning in isolation, a single predictive model for a task using a dataset. |
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Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020.acl-main)
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Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, Samuel R. Bowman
| Challenge: | Unsupervised pretraining has recently pushed the state of the art on many natural language understanding tasks. |
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Is BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models’ Transferability (2021.findings-emnlp)
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| Challenge: | Using pre-trained language models, we can apply them to specialized domains such as scientific articles or clinical data. |
| Approach: | They propose to pre-train BERT models on large text corpora and use them to generalize to token sequence classification applications. |
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Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
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Commonsense Knowledge Transfer for Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Recent advances in pre-trained language models have transformed the landscape of natural language processing. |
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An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks (2022.emnlp-main)
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| Challenge: | Recent work attempts to explicitly incorporate human-defined linguistic priors into fine-tuning tasks. |
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BERTese: Learning to Speak to BERT (2021.eacl-main)
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| Challenge: | Recent work shows that pre-trained language models encode large amounts of world knowledge in their parameters. |
| Approach: | They propose a method for automatically rewriting queries into a paraphrase query called "BERTese" they add auxiliary loss functions that encourage the query to correspond to actual language tokens . |
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